<![CDATA[The increasing complexity and volume of cyber threats have placed significant pressure on traditional incident response (IR) systems, necessitating the adoption of more advanced technologies to detect, analyze, and mitigate attacks efficiently. One such technology is machine learning (ML), which offers the potential to transform incident response by automating threat detection, prioritizing incidents, and dynamically adjusting responses based on evolving attack patterns. This paper explores the integration of machine learning into intelligent incident response systems, focusing on its applications, benefits, and challenges. Through an in-depth examination of machine learning techniques—such as supervised learning, unsupervised learning, deep learning, and reinforcement learning—we highlight how these models can enhance various stages of incident response, including detection, triage, automated remediation, and post-incident analysis. Additionally, we discuss case studies showcasing the effectiveness of ML in real-world IR scenarios and identify key challenges, such as data quality, adversarial attacks, and model interpretability. The paper also proposes potential future directions, including hybrid ML models, human-in-the-loop systems, and advances in explainable AI, to further improve the reliability and transparency of ML-driven IR systems. Ultimately, this research aims to provide a comprehensive understanding of how machine learning can augment incident response efforts and enhance cybersecurity resilience in the face of increasingly sophisticated threats.]]>
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